The present invention relates to artificial neural networks, and more specifically, to electronic learning synapses with spike-dependent plasticity.
The point of contact between an axon of a neuron and a dendrite on another neuron is called a synapse, and with respect to the synapse, the two neurons are respectively called pre-synaptic and post-synaptic. The essence of our individual experiences is stored in conductance of the synapses. The synaptic conductance changes with time as a function of the relative spike times of pre- and post-synaptic neurons as per spike-timing dependent plasticity (STDP). The STDP rule increases the conductance of a synapse if its post-synaptic neuron fires after its pre-synaptic neuron fires, and decreases the conductance of a synapse if the order of two firings is reversed. Further, the change depends on the precise delay between the two events: the more the delay, the less the magnitude of change.
Artificial neural networks are computational systems that permit computers to essentially function in a manner analogous to that of biological brains. Artificial neural networks do not utilize the traditional digital model of manipulating 0s and 1s. Instead, they create connections between processing elements, which are equivalent to neurons of a human brain. Artificial neural networks may be based on various electronic circuits that are modeled on neurons.